Scaffold-Grown Tumor Cells to Personalize Cancer Treatments

A spinoff from the University of Oklahoma is developing a better way to design and 3D print artificial scaffolds, so they can rapidly grow tumor cells taken from individuals with cancer. This would enable clinicians to test which combination of chemotherapy drugs might best suit the individual and to begin treatment earlier.

Cancer patients, even those with similar diagnoses, often respond differently to the same drugs. Until recently, clinicians worked primarily by trial and error, testing different combinations of drugs to see how patients responded.

By testing different drug mixtures and strengths against arrays of lab-grown tumor cells, oncologists could hit upon the most promising chemotherapy combinations more rapidly, reduce treatment times, and improve outcomes. Tumor cells for testing are already available, since clinicians collect them during routine biopsies. But to run large arrays of tests, they need to grow many more tumor cells outside patient bodies.

Researchers typically grow cells on scaffolds, which are not easy to build. Body tissues contain a myriad of mechanical and biochemical signals that determine how tumors will reproduce and spread. A scaffold needs to replicate these cues, so researchers will have a better idea how a medicine will affect tumor cells in their natural environment, said Cortes Williams, a postdoctoral researcher at the University of Oklahoma.

Williams is developing 3D printed scaffolds that mimic natural tissue on which he can grow different types of tumor cells. While many others have done the same, Williams focuses on speed. The faster the cells grow, the sooner doctors can begin optimized treatments.

Many other researchers opt for hydrogel-based tissue scaffolds that contain collagen proteins and growth factors that tumor cells typically see in the body. These scaffolds mimic the chemical signals of tissue well, but they rely on diffusion to transport nutrients, which limits how fast cells can grow.

Williams’ way around this limit is to use harder polymer frameworks built to improve flow. His scaffolds look a bit like tiny cheese graters. The nutrient bath flows through the pores in the grater-like structure, increasing the flow of nutrients to tumor cells better than diffusion alone. To make cells feel at home atop foreign polymers, he functionalizes the scaffolds with a proprietary combination of proteins and chemicals, encouraging cell attachment and growth.

Tuning the architecture of the 3D-printed scaffolds enables Williams to grow different types of cancer cells. Pore size, thickness of the pillars between pores, and the printing material all affect the forces on the cells. Bone cancer cells, for example, respond better to more force, while breast cancer cells need a softer environment.

Others have done similar, but they are unable to grow tumor cells consistently and repeatably. The problem, Williams said, is that 3D printers rarely print the exact structures specified in CAD files. This is especially true when dealing with small structures designed to stabilize cells. These test the resolution of many 3D printers.

As a result, forces that might appear optimized when simulating the behavior of a CAD design are anything but on the printed part. Williams points to small printing defects—excess material laid down when nozzle turns, and variable congealing between layers—that add up to large changes in the cellular environment. As a result, simulations often underestimate the shear forces present on an actual scaffold, Williams said.

To improve his predictions of the forces on as-printed parts, Williams scans his 3D printed scaffolds using micro-CT, a high-resolution version of computed tomography. He then incorporates this data (in the form of a probability density function) into is CAD routine to generate design results that more closely match his real-world tests. He has collected scan data from a few different 3D printing machines in order to address different flavors of variation. The result, he said, is a more consistent growth platform.

Bioreactors, where cells grow on scaffolds, also introduce some variation in flows, and Williams is keen to address this, too. His modeling software also accounts for four different bioreactors, enabling him to use a variety of lab setups to produce the same repeatable results.

Williams’ startup, Advanced Culture Systems, has secured a provisional patent for the technology that includes scaffold functionalization and force prediction. He ultimately hopes his company will earn FDA approval to print functionalized scaffolds and provide testing for clinics.

Given a sample from a patient’s biopsy, the company will grow tumor cells in bioreactors, pelt those samples with different drugs, and send results to doctors within a few weeks. “In a quicker timescale, for cheaper—we’re giving them information that they didn’t have access to before,” he said.

The technology’s greatest value will be to highlight which treatments to avoid, Williams said. Without knowing the response of a patient’s immune system, no test can fully determine which drugs will work in the body. But physicians can rule out drugs that scaffold-grown tumors ignore.

Between the genetic data clinicians analyze and this specific, individualized testing, more doctors may eventually prescribe treatments tailored to specific cancer cells. Although drugs prescribed to each patient may vary more, targeted care could unify treatment responses, ensuring that more chemotherapy courses end in recovery.